Installation¶
Requirements
A from-scratch setup script
Prepare environment
Run with docker image
Test environment
Frequently Asked Questions
Requirements¶
Linux
ffmpeg
Python 3.7+
PyTorch 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.8.1, 1.9.0 or 1.9.1.
CUDA 9.2+
GCC 5+
Optional:
| Name | When it is required | What's important |
|---|---|---|
| MMPose | Keypoints 2D estimation. | Install mmcv-full, instead of mmcv. |
| MMDetection | Bbox 2D estimation. | Install mmcv-full, instead of mmcv. |
| MMTracking | Multiple object tracking. | Install mmcv-full, instead of mmcv. |
| Aniposelib | Triangulation. | Install from github, instead of pypi. |
A from-scratch setup script¶
conda create -n xrmocap python=3.8
source activate xrmocap
# install ffmpeg for video and images
conda install -y ffmpeg
# install pytorch
conda install -y pytorch==1.8.1 torchvision==0.9.1 cudatoolkit=10.1 -c pytorch
# install pytorch3d
conda install -y -c fvcore -c iopath -c conda-forge fvcore iopath
conda install -y -c bottler nvidiacub
conda install -y pytorch3d -c pytorch3d
# install mmcv-full
pip install mmcv-full==1.5.3 -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.8.1/index.html
# install xrprimer
pip install xrprimer
# clone xrmocap
git clone https://github.com/openxrlab/xrmocap.git
cd xrmocap
# install requirements for build
pip install -r requirements/build.txt
# install requirements for runtime
pip install -r requirements/runtime.txt
# install xrmocap
rm -rf .eggs && pip install -e .
Prepare environment¶
Here are advanced instructions for environment setup. If you have run A from-scratch setup script successfully, please skip this.
a. Create a conda virtual environment and activate it.¶
conda create -n xrmocap python=3.8 -y
conda activate xrmocap
b. Install MMHuman3D.¶
Here we take torch_version=1.8.1 and cu_version=10.2 as example. For other versions, please follow the official instructions
# install ffmpeg from main channel
conda install ffmpeg
# install pytorch
conda install -y pytorch==1.8.1 torchvision==0.9.1 cudatoolkit=10.2 -c pytorch
# install pytorch3d
conda install -c fvcore -c iopath -c conda-forge fvcore iopath -y
conda install -c bottler nvidiacub -y
conda install pytorch3d -c pytorch3d
# install mmcv-full for human_perception
pip install mmcv-full==1.5.3 -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.1/index.html
# install mmhuman3d
pip install git+https://github.com/open-mmlab/mmhuman3d.git
Note1: Make sure that your compilation CUDA version and runtime CUDA version match.
Note2: The package mmcv-full(gpu) is essential if you are going to use human_perception modules.
Note3: Do not install optional requirements of mmhuman3d in this step.
c. Install XRPrimer.¶
pip install xrprimer
If you want to edit xrprimer, please follow the official instructions to install it from source.
d. Install XRMoCap to virtual environment, in editable mode.¶
git clone https://github.com/openxrlab/xrmocap.git
cd xrmocap
pip install -r requirements/build.txt
pip install -r requirements/runtime.txt
pip install -e .
e. Run unittests or demos¶
If everything goes well, try to run unittest or go back to run demos
Run with Docker Image¶
We provide a Dockerfile to build an image. Ensure that you are using docker version >=19.03 and "default-runtime": "nvidia" in daemon.json.
# build an image with PyTorch 1.8.1, CUDA 10.2
docker build -t xrmocap .
Run it with
docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/xrmocap/data xrmocap
Or pull a built image from docker hub.
docker pull openxrlab/xrmocap_runtime
docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/xrmocap/data openxrlab/xrmocap_runtime